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All Journal Jurnal Konseling dan Pendidikan Journal of Moeslim Research Technik Techno Agriculturae Studium of Research Journal of Biomedical and Techno Nanomaterials Journal of Social Entrepreneurship and Creative Technology Journal of Multidisciplinary Sustainability Asean Synergisia Journal Neosantara Hybrid Learning
Ardi Azhar Nampira
Institute Teknologi Sepuluh November
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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AI In Diplomacy: An Analysis Of The Role Of Artificial Intelligence In Multilateral Negotiations Loso Judijanto; Lutpi Samaduri; Ardi Azhar Nampira
Synergisia Vol. 2 No. 1 (2025): Synergisia - MAY
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/hkc0nc06

Abstract

This research analyzes the role of artificial intelligence (AI) in multilateral diplomacy, with a focus on improving the efficiency and effectiveness of international negotiations. Applying a qualitative approach through literature research methods, this analysis collects and synthesizes relevant literature, including books, scientific journals, and policy reports that discuss the interaction between AI and diplomacy. AI has the potential to improve real-time data analysis, sentiment tracking, as well as predict the outcome of diplomatic scenarios, enabling diplomats to be more responsive to complex global challenges. However, the use of AI is also faced with ethical and security challenges, such as algorithmic bias, transparency issues, and the potential for a reduction in the role of humans in diplomatic decision-making. Therefore, it is important to develop a regulatory framework that ensures fair and transparent use of AI in the context of diplomacy. The study provides recommendations to stakeholders on the importance of human oversight and strong governance to optimize the benefits of AI in multilateral diplomacy, while also considering its ethical implications
Enhancing augmented reality experiences through advanced computer vision techniques Chairuddin Chairuddin; Ardi Azhar Nampira; Jarot Budiasto
Jurnal Konseling dan Pendidikan Vol. 13 No. 3 (2025): JKP
Publisher : Indonesian Institute for Counseling, Education and Therapy (IICET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29210/1171200

Abstract

This study investigates the integration of advanced computer vision techniques, specifically semantic segmentation, depth estimation, and object detection, into augmented reality (AR) systems to enhance user immersion, contextual interaction, and real-world adaptability. The research aims to address the limitations of existing AR systems, which often struggle with real-time performance and context-aware interaction in dynamic environments. Using a design-based research (DBR) approach, the study involved testing the proposed AR system on mobile devices under controlled and real-world conditions. Performance was assessed using specific metrics: frame rate (FPS), inference latency, semantic accuracy, and user experience. The sample included 20 participants, recruited through convenience sampling, with inclusion criteria focused on individuals familiar with mobile AR applications. Ethical approval was obtained for the study, and informed consent was provided by all participants. The testing involved both simulation trials and real-world prototyping in varied environmental conditions, such as indoor and outdoor settings with different lighting and motion dynamics. Results indicate that the integrated of the vision stack significantly enhanced scene understanding and enabling stable, context-aware digital overlays, with the system maintaining a real-time frame rate of >27 FPS. User feedback, measured through a 5-point Likert scale survey, confirmed improved immersion, visual coherence, and satisfaction compared to baseline AR systems, with an average increase of 35% in perceived realism. The analysis also revealed that the system's performance remained consistent across varying environmental conditions, with minimal latency (less than 300ms) in dynamic re-anchoring of AR elements. Statistical tests (paired t-tests) confirmed the significance of these improvements, with p-values < 0.05 for all key metrics. This research contributes a scalable framework that bridges artificial intelligence, user experience design, and mobile AR deployment. It provides empirical evidence supporting the integration of computer vision techniques into AR systems, with practical implications for applications in education, healthcare, and industry. Future work will focus on expanding the user base, exploring hardware compatibility, and investigating multimodal AR interactions.
IOT-BASED SOLAR POWER GENERATION SYSTEM DESIGN FOR REAL-TIME MONITORING Farida Arinie; Sulaiman Sulaiman; Usman Tahir; Nurjannah Nurjannah; Ardi Azhar Nampira
Journal of Moeslim Research Technik Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i1.1932

Abstract

The increasing demand for renewable energy sources has led to the growing adoption of solar power systems. However, efficient monitoring of these systems is essential for optimizing performance and maintenance. Integrating Internet of Things (IoT) technology offers potential solutions for real-time monitoring and management of solar power generation. This research aims to design an IoT-based solar power generation system that enables real-time monitoring of energy production, system performance, and environmental conditions. The goal is to enhance the efficiency and reliability of solar energy systems through advanced data analytics. A prototype system was developed using IoT sensors to collect data on solar panel output, temperature, and weather conditions. The system utilized a microcontroller for data processing and transmission to a cloud platform for real-time visualization and analysis. User-friendly dashboards were created to facilitate monitoring and alert users to potential issues. The findings demonstrated that the IoT-based system effectively monitored solar power generation, providing real-time data on energy output and environmental factors. The system achieved an accuracy of 95% in data reporting, allowing for timely interventions to optimize performance. Users reported improved decision-making capabilities based on the insights gained from the monitoring system.  
EFFICIENCY OF WIRELESS CHARGING SYSTEMS IN HIGH-SPEED ELECTRIC VEHICLES Taryana Taryana; Chak Sothy; Ardi Azhar Nampira
Journal of Moeslim Research Technik Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i2.1933

Abstract

The increasing adoption of electric vehicles (EVs) necessitates the development of efficient charging solutions. Wireless power transfer (WPT) technology has emerged as a promising method for enhancing the convenience and efficiency of EV charging. Understanding the efficiency of WPT systems in high-speed charging applications is critical for their widespread implementation. This research aims to evaluate the efficiency of wireless charging systems for high-speed electric vehicles. The study investigates various factors affecting energy transfer efficiency, including alignment, distance, and frequency of operation. An experimental setup was created to test a wireless charging system under controlled conditions. Efficiency measurements were taken at different distances and alignments between the transmitter and receiver coils. Data were analyzed to identify optimal operating conditions and performance metrics. The findings indicated that the wireless charging system achieved an overall efficiency of 85% under ideal conditions. Efficiency decreased with increased distance between the coils, with a notable drop at distances exceeding 20 cm. Optimal alignment was found to enhance energy transfer, significantly improving overall system performance. The study demonstrates that wireless charging systems can be efficient for high-speed electric vehicles, with potential for practical applications in urban environments. These findings highlight the importance of optimizing system design and alignment to maximize efficiency.
DEVELOPMENT OF AN INTEGRATED COMMUNICATION SYSTEM FOR 5G-BASED AUTONOMOUS VEHICLES Alkautsar Rahman; Felipe Souza; Raul Gomez; Rahmi Setiawati; Ardi Azhar Nampira
Journal of Moeslim Research Technik Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i1.1934

Abstract

The rapid advancement of autonomous vehicle technology necessitates robust communication systems to ensure safety, efficiency, and connectivity. The emergence of 5G technology presents opportunities to enhance communication capabilities for autonomous vehicles, enabling real-time data exchange and improved decision-making. This research aims to develop an integrated communication system for autonomous vehicles utilizing 5G technology. The study focuses on evaluating the performance, reliability, and latency of the proposed system in various driving scenarios. An experimental approach was employed, involving the design and implementation of a 5G-based communication framework for autonomous vehicles. Various tests were conducted in controlled environments to assess communication latency, data throughput, and system reliability. Different vehicular scenarios, including urban and highway driving, were simulated to evaluate performance under diverse conditions. The findings indicated that the integrated 5G communication system achieved a latency of less than 10 milliseconds, significantly enhancing real-time data transmission. Data throughput exceeded 1 Gbps, demonstrating the capability to support high-bandwidth applications. The system exhibited robust performance across various driving scenarios, with minimal data loss and high reliability. The research demonstrates the potential of 5G technology in transforming communication systems
THE EFFECT OF AERODYNAMIC DESIGN ON FUEL EFFICIENCY IN COMMERCIAL VEHICLES Carlos Fernandez; Siti Shofiah; Ardi Azhar Nampira
Journal of Moeslim Research Technik Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i2.1936

Abstract

The increasing demand for fuel efficiency in commercial vehicles has prompted extensive research into aerodynamic designs. Improved aerodynamics can significantly reduce drag, leading to enhanced fuel economy and lower operational costs for commercial fleets. Understanding the relationship between aerodynamic design and fuel efficiency is critical for optimizing vehicle performance. This research aims to evaluate the impact of various aerodynamic designs on the fuel efficiency of commercial vehicles. The study focuses on analyzing the performance differences between conventional and streamlined vehicle shapes. An experimental approach was employed, utilizing computational fluid dynamics (CFD) simulations alongside real-world driving tests. Several vehicle models with different aerodynamic features were tested under controlled conditions. Fuel consumption data was collected and analyzed to assess the relationship between design modifications and fuel efficiency. The findings indicated that streamlined designs improved fuel efficiency by an average of 15% compared to conventional models. Vehicles with enhanced aerodynamic features experienced reduced drag coefficients, leading to significant fuel savings during operation. The results demonstrated a clear correlation between aerodynamic optimization and improved fuel economy. The research highlights the crucial role of aerodynamic design in enhancing fuel efficiency for commercial vehicles. These findings emphasize the importance of integrating aerodynamic considerations into vehicle design processes.
APPLICATION OF INTERNET OF THINGS (IOT) IN MODERN LIVESTOCK MANAGEMENT IN NEW ZEALAND Dina Destari; Raul Gomez; Bruna Costa; Ardi Azhar Nampira
Techno Agriculturae Studium of Research Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i1.1991

Abstract

This study examines the application of Internet of Things (IoT) technology in modern livestock management in New Zealand. The background of this research is based on the need to increase productivity, efficiency, and sustainability in the increasingly competitive livestock sector. The purpose of the study is to explore the benefits of applying IoT in livestock health monitoring, feed management, as well as the impact of this technology on the environment and sustainability. The research method used is descriptive-qualitative with data collection through interviews, field observations, and secondary data analysis. The results show that the adoption of IoT in large farms increases productivity by up to 20% and reduces operational costs through more efficient feed management. The study also found that infrastructure challenges are a hindrance to IoT adoption in small and medium-sized farms. The conclusion of the study is that IoT has the potential to be a key solution to improve efficiency and sustainability in the livestock sector, but infrastructure support and training are urgently needed to accelerate its adoption across sectors.
PLANT HEALTH MONITORING TECHNOLOGY WITH ARTIFICIAL INTELLIGENCE IN FRANCE Faisal Razak; Rina Farah; Haziq Idris; Ardi Azhar Nampira
Techno Agriculturae Studium of Research Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i2.1995

Abstract

This study explores the role of artificial intelligence (AI)-based plant health monitoring technology in France, which is expected to improve the efficiency of early detection of plant diseases and optimize the use of agricultural resources. The background of this research is based on the urgent need to increase agricultural productivity and reduce negative impacts on the environment. The purpose of this study is to test the effectiveness of AI in detecting plant health problems and provide data-driven recommendations for farmers. This study uses a mixed approach, with quantitative data from farmer surveys and qualitative data from interviews and case studies in major agricultural regions in France. The results showed that 80% of farmers reported an increase in early detection of diseases, and 75% reported a reduction in pesticide use. In conclusion, AI is playing an important role in supporting sustainable agriculture in France, although challenges in access to technology still need to be addressed. Further research is needed to explore ways to expand the adoption of this technology among smallholders.
AI POWERED DIGITAL HISTOPATHOLOGY: PREDICTING IMMUNOTHERAPY RESPONSE USING DEEP LEARNING Loso Judijanto; Som Chai; Ming Pong; Justam Justam; Ardi Azhar Nampira
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2379

Abstract

Immunotherapy has revolutionized cancer treatment, yet predicting which patients will respond remains a major clinical challenge. Current predictive biomarkers, such as PD-L1 expression, have limited accuracy and fail to capture the complex interplay of cells within the tumor microenvironment. Digital histopathology, the analysis of digitized tissue slides, combined with artificial intelligence (AI), offers a novel approach to identify complex morphological patterns that could serve as more robust predictive biomarkers. Objective: A deep learning model, specifically a convolutional neural network (CNN), was trained on a large, multi-center cohort of digitized tumor slides from patients with non-small cell lung cancer who had received ICI therapy. The model was trained to identify subtle morphological features and the spatial arrangement of tumor cells and tumor-infiltrating lymphocytes. The model’s predictive performance was rigorously validated on an independent, held-out test cohort, and its performance was compared to the predictive accuracy of PD-L1 staining. The AI-powered model successfully predicted immunotherapy response with a high degree of accuracy, achieving an area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort.
AI ASSISTED PERSONALIZED VACCINE DESIGN USING MULTI-OMICS CANCER DATA Khalil Zaman; Shazia Akhtar; Sofia Lim; Ardi Azhar Nampira
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2381

Abstract

The development of personalized cancer vaccines represents a promising frontier in oncology, yet traditional approaches struggle with the complexity and volume of multi-omics data. This study addresses this challenge by introducing an AI-assisted framework for the design of personalized vaccines. The primary objective was to leverage machine learning models to identify and prioritize neoantigens from integrated genomic, transcriptomic, and proteomic data of cancer patients. The methodology involved a deep learning pipeline to analyze multi-omics datasets, predicting tumor-specific mutations and their immunogenicity. This was followed by an algorithm to select the most potent neoantigen peptides for vaccine formulation, optimizing for both MHC binding affinity and T-cell activation potential. Our results demonstrate that the AI-driven approach significantly improved the speed and accuracy of neoantigen identification compared to conventional methods. The framework successfully predicted a set of high-quality vaccine candidates for individual patients, which showed strong in silico binding to patient-specific MHC molecules. We conclude that this AI-assisted methodology provides a powerful and scalable solution for personalized vaccine design, accelerating the translation of multi-omics data into clinically actionable immunotherapies.
Co-Authors Alkautsar Rahman Bruna Costa Carlos Fernandez Chairuddin Chak Sothy Dara Vann Dina Destari Faisal Razak Farida Arinie Felipe Souza Haruka Sato Haziq Idris Jarot Budiasto Justam Justam Khalil Zaman Loso Judijanto Lutpi Samaduri Miku Fujita Ming Pong Nurjannah Nurjannah Rahmi Setiawati Raul Gomez Rina Farah Shazia Akhtar Siti Mariam Siti Shofiah Sofia Lim Som Chai Sulaiman Sulaiman Usman Tahir Van Sok Widi Harto Purnomo